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# Counting the total number of missing values (NaNs) of a Pandas DataFrame

Pandas
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Handling Missing Values
schedule Jul 1, 2022
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# Example

Consider the following DataFrame with some `NaN` values:

``` df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,np.nan,5],"C":[np.nan,7,8]}, index=["a","b","c"])df    A    B    Ca  NaN  4.0  NaNb  3.0  NaN  7.0c  NaN  5.0  8.0 ```

## Solution

To count the total number of `NaN` values in `df`:

``` df.isna().values.sum() 4 ```

## Explanation

Here, the `df.isna()` returns a DataFrame of booleans where `True` indicates entries that are `NaN`:

``` df.isna()    A      B      Ca  True   False  Trueb  False  True   Falsec  True   False  False ```

Internally, `True` is represented by `1` while a `False` is represented by `0`. Therefore, summing up all the values of `df` would then tell us how many `NaN` values there are.

The problem with the DataFrame's `sum()` method is that we can only compute the sum of each row or column of `df`. What we want to do instead is to compute the sum of all the values of the DataFrame.

We can do this by extracting the values of `df` as a NumPy array via the `values` property, and then calling its `sum()` method:

``` df.isna().values.sum() 4 ```
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